Skip to main content

Advertisement

Log in

Design and application of single-cell RNA sequencing to study kidney immune cells in lupus nephritis

  • Review Article
  • Published:

From Nature Reviews Nephrology

View current issue Sign up to alerts

Abstract

The immune mechanisms that cause tissue injury in lupus nephritis have been challenging to define. The advent of high-dimensional cellular analyses, such as single-cell RNA sequencing, has enabled detailed characterization of the cell populations present in small biopsy samples of kidney tissue. In parallel, the development of methods that cryopreserve kidney biopsy specimens in a manner that preserves intact, viable cells, has enabled the uniform analysis of tissue samples collected at multiple sites and across many geographic areas and demographic cohorts with high-dimensional platforms. The application of these methods to kidney biopsy samples from patients with lupus nephritis has begun to define the phenotypes of both infiltrating and resident immune cells, as well as parenchymal cells, present in nephritic kidneys. The detection of similar immune cell populations in urine suggests that it might be possible to non-invasively monitor immune activation in kidneys. Once applied to large patient cohorts, these high-dimensional studies might enable patient stratification according to patterns of immune cell activation in the kidney or identify disease features that can be used as surrogate measures of efficacy in clinical trials. Applied broadly across multiple inflammatory kidney diseases, these studies promise to enormously expand our understanding of renal inflammation in the next decade.

Key points

  • Single-cell RNA sequencing (scRNA-seq) has begun to define the phenotypes of distinct immune cell populations that accumulate in the kidney in lupus nephritis; these populations can be assessed across patients to identify molecularly distinct disease mechanisms and inform precision medicine strategies.

  • Design of cohort studies of lupus nephritis using scRNA-seq must balance the desire for optimal samples with feasibility and must consider aspects of clinical heterogeneity including disease duration, and patient gender, ethnicity and drug treatment in enrolment criteria.

  • Cryopreservation of tissue biopsies allows the accumulation of a biorepository of tissue containing intact cells, which can be analysed uniformly in batches to reduce technical variation.

  • Comparison of immune cells in the kidney with cells from urine and blood might identify cellular markers that can be measured in these biological samples reducing the need for invasive kidney biopsies.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1: Implementation of scRNA-seq to study lupus nephritis kidney biopsy samples.
Fig. 2: Towards a cellular classification of renal immunopathology.
Fig. 3: Insights from scRNA-seq analysis of the renal immune cell compartment in lupus nephritis.

Similar content being viewed by others

References

  1. Almaani, S., Meara, A. & Rovin, B. H. Update on lupus nephritis. Clin. J. Am. Soc. Nephrol. 12, 825–835 (2017).

    Article  PubMed  Google Scholar 

  2. Costenbader, K. H. et al. Trends in the incidence, demographics, and outcomes of end-stage renal disease due to lupus nephritis in the US from 1995 to 2006. Arthritis Rheum. 63, 1681–1688 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Hanly, J. G. et al. The frequency and outcome of lupus nephritis: results from an international inception cohort study. Rheumatology 55, 252–262 (2016).

    Article  PubMed  Google Scholar 

  4. Jorge, A. et al. All-cause and cause-specific mortality trends of end-stage renal disease due to lupus nephritis from 1995 to 2014. Arthritis Rheumatol. 71, 403–410 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Narain, S. & Furie, R. Update on clinical trials in systemic lupus erythematosus. Curr. Opin. Rheumatol. 28, 477–487 (2016).

    Article  CAS  PubMed  Google Scholar 

  6. Vandepapeliere, J. et al. Prognosis of proliferative lupus nephritis subsets in the Louvain lupus nephritis inception cohort. Lupus 23, 159–165 (2014).

    Article  CAS  PubMed  Google Scholar 

  7. Yap, D. Y., Tang, C. S., Ma, M. K., Lam, M. F. & Chan, T. M. Survival analysis and causes of mortality in patients with lupus nephritis. Nephrol. Dial. Transpl. 27, 3248–3254 (2012).

    Article  Google Scholar 

  8. Haridasan, S., Sharma, A. & Rathi, M. Treatment of membranous lupus nephritis. Clin. Queries Nephrology 3, 106–113 (2014).

    Article  Google Scholar 

  9. Ayodele, O. E., Okpechi, I. G. & Swanepoel, C. R. Predictors of poor renal outcome in patients with biopsy-proven lupus nephritis. Nephrology 15, 482–490 (2010).

    Article  PubMed  Google Scholar 

  10. Broder, A. et al. Tubulointerstitial damage predicts end stage renal disease in lupus nephritis with preserved to moderately impaired renal function: a retrospective cohort study. Semin. Arthritis Rheum. 47, 545–551 (2018).

    Article  PubMed  Google Scholar 

  11. Hsieh, C. et al. Predicting outcomes of lupus nephritis with tubulointerstitial inflammation and scarring. Arthritis Care Res. 63, 865–874 (2011).

    Article  Google Scholar 

  12. Hill, G. S., Delahousse, M., Nochy, D., Mandet, C. & Bariety, J. Proteinuria and tubulointerstitial lesions in lupus nephritis. Kidney Int. 60, 1893–1903 (2001).

    Article  CAS  PubMed  Google Scholar 

  13. Weening, J. J. et al. The classification of glomerulonephritis in systemic lupus erythematosus revisited. J. Am. Soc. Nephrol. 15, 241–250 (2004).

    Article  PubMed  Google Scholar 

  14. Kassianos, A. J. et al. Increased tubulointerstitial recruitment of human CD141(hi) CLEC9A(+) and CD1c(+) myeloid dendritic cell subsets in renal fibrosis and chronic kidney disease. Am. J. Physiol. Ren. Physiol 305, F1391–F1401 (2013).

    Article  CAS  Google Scholar 

  15. Rovin, B. H., Parikh, S. V. & Alvarado, A. The kidney biopsy in lupus nephritis: is it still relevant? Rheum. Dis. Clin. North. Am. 40, 537–552, ix (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Schwartz, M. M. et al. Irreproducibility of the activity and chronicity indices limits their utility in the management of lupus nephritis. Am. J. Kidney Dis. 21, 374–377 (1993).

    Article  CAS  PubMed  Google Scholar 

  17. Teh, C. L. et al. Causes and predictors of mortality in biopsy-proven lupus nephritis: the Sarawak experience. Clin. Kidney J. 11, 56–61 (2018).

    Article  PubMed  Google Scholar 

  18. Shen, Y. et al. Association of intrarenal B-cell infiltrates with clinical outcome in lupus nephritis: a study of 192 cases. Clin. Dev. Immunol. 2012, 967584 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Schelling, J. R. Tubular atrophy in the pathogenesis of chronic kidney disease progression. Pediatr. Nephrol. 31, 693–706 (2016).

    Article  PubMed  Google Scholar 

  20. Malvar, A. et al. Histologic versus clinical remission in proliferative lupus nephritis. Nephrol. Dial. Transpl. 32, 1338–1344 (2017).

    Article  CAS  Google Scholar 

  21. Alvarado, A. S. et al. The value of repeat kidney biopsy in quiescent Argentinian lupus nephritis patients. Lupus 23, 840–847 (2014).

    Article  CAS  PubMed  Google Scholar 

  22. Zickert, A., Sundelin, B., Svenungsson, E. & Gunnarsson, I. Role of early repeated renal biopsies in lupus nephritis. Lupus Sci. Med. 1, e000018 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Dall’Era, M. et al. Predictors of long-term renal outcome in lupus nephritis trials: lessons learned from the Euro-Lupus Nephritis cohort. Arthritis Rheumatol. 67, 1305–1313 (2015).

    Article  CAS  PubMed  Google Scholar 

  24. Mackay, M. et al. Establishing surrogate kidney end points for lupus nephritis clinical trials: development and validation of a novel approach to predict future kidney outcomes. Arthritis Rheumatol. 71, 411–419 (2019).

    Article  CAS  PubMed  Google Scholar 

  25. Bennett, L. et al. Interferon and granulopoiesis signatures in systemic lupus erythematosus blood. J. Exp. Med. 197, 711–723 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Baechler, E. C. et al. Interferon-inducible gene expression signature in peripheral blood cells of patients with severe lupus. Proc. Natl Acad. Sci. USA 100, 2610–2615 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Felten, R., Scher, F., Sagez, F., Chasset, F. & Arnaud, L. Spotlight on anifrolumab and its potential for the treatment of moderate-to-severe systemic lupus erythematosus: evidence to date. Drug Des. Devel. Ther. 13, 1535–1543 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. ACCESS Trial Group Treatment of lupus nephritis with abatacept: the Abatacept and Cyclophosphamide Combination Efficacy and Safety Study. Arthritis Rheumatol. 66, 3096–3104 (2014).

    Article  CAS  Google Scholar 

  29. Berthier, C. C. et al. Cross-species transcriptional network analysis defines shared inflammatory responses in murine and human lupus nephritis. J. Immunol. 189, 988–1001 (2012).

    Article  CAS  PubMed  Google Scholar 

  30. Bethunaickan, R., Berthier, C. C., Zhang, W., Kretzler, M. & Davidson, A. Comparative transcriptional profiling of 3 murine models of SLE nephritis reveals both unique and shared regulatory networks. PLOS ONE 8, e77489 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Wilson, P. C. et al. The single-cell transcriptomic landscape of early human diabetic nephropathy. Proc. Natl Acad. Sci. USA 116, 19619–19625 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Wu, H. et al. Single-cell transcriptomics of a human kidney allograft biopsy specimen defines a diverse inflammatory response. J. Am. Soc. Nephrol. 29, 2069–2080 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Bassler, K., Schulte-Schrepping, J., Warnat-Herresthal, S., Aschenbrenner, A. C. & Schultze, J. L. The myeloid cell compartment–cell by cell. Annu. Rev. Immunol. 37, 269–293 (2019).

    Article  CAS  PubMed  Google Scholar 

  34. Mueller, S. N., Gebhardt, T., Carbone, F. R. & Heath, W. R. Memory T cell subsets, migration patterns, and tissue residence. Annu. Rev. Immunol. 31, 137–161 (2013).

    Article  CAS  PubMed  Google Scholar 

  35. Birnbaum, K. D. Power in numbers: single-cell RNA-Seq strategies to dissect complex tissues. Annu. Rev. Genet. 52, 203–221 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Pamfil, C. et al. Intrarenal activation of adaptive immune effectors is associated with tubular damage and impaired renal function in lupus nephritis. Ann. Rheum. Dis. 77, 1782–1789 (2018).

    Article  CAS  PubMed  Google Scholar 

  37. Liarski, V. M. et al. Cell distance mapping identifies functional T follicular helper cells in inflamed human renal tissue. Sci. Transl Med. 6, 230ra46 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Ju, W. et al. Tissue transcriptome-driven identification of epidermal growth factor as a chronic kidney disease biomarker. Sci. Transl Med. 7, 316ra193 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Berthier, C. C., Kretzler, M. & Davidson, A. A systems approach to renal inflammation in SLE. Clin. Immunol. 185, 109–118 (2017).

    Article  CAS  PubMed  Google Scholar 

  40. Parikh, S. V. et al. Molecular imaging of the kidney in lupus nephritis to characterize response to treatment. Transl. Res. 182, 1–13 (2017).

    Article  CAS  PubMed  Google Scholar 

  41. Parikh, S. V. et al. Characterising the immune profile of the kidney biopsy at lupus nephritis flare differentiates early treatment responders from non-responders. Lupus Sci. Med. 2, e000112 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Peterson, K. S. et al. Characterization of heterogeneity in the molecular pathogenesis of lupus nephritis from transcriptional profiles of laser-captured glomeruli. J. Clin. Invest. 113, 1722–1733 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Wang, X., Park, J., Susztak, K., Zhang, N. R. & Li, M. Bulk tissue cell type deconvolution with multi-subject single-cell expression reference. Nat. Commun. 10, 380 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Park, J. et al. Single-cell transcriptomics of the mouse kidney reveals potential cellular targets of kidney disease. Science 360, 758–763 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Young, M. D. et al. Single-cell transcriptomes from human kidneys reveal the cellular identity of renal tumors. Science 361, 594–599 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Alles, J. et al. Cell fixation and preservation for droplet-based single-cell transcriptomics. BMC Biol. 15, 44 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Roccatello, D. et al. Outpatient percutaneous native renal biopsy: safety profile in a large monocentric cohort. BMJ Open 7, e015243 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Leek, J. T. et al. Tackling the widespread and critical impact of batch effects in high-throughput data. Nat. Rev. Genet. 11, 733–739 (2010).

    Article  CAS  PubMed  Google Scholar 

  49. Nguyen, Q. H., Pervolarakis, N., Nee, K. & Kessenbrock, K. Experimental considerations for single-cell RNA sequencing approaches. Front. Cell Dev. Biol. 6, 108 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Tung, P. Y. et al. Batch effects and the effective design of single-cell gene expression studies. Sci. Rep. 7, 39921 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Arazi, A. et al. The immune cell landscape in kidneys of patients with lupus nephritis. Nat. Immunol. 20, 902–914 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Rao, D. A. et al. A protocol for single cell transcriptomics from cryopreserved renal tissue and urine for the Accelerating Medicines Partnership (AMP) RA/SLE network. Preprint at bioRxiv https://doi.org/10.1101/275859 (2018).

  53. Beliakova-Bethell, N. et al. The effect of cell subset isolation method on gene expression in leukocytes. Cytometry A 85, 94–104 (2014).

    Article  CAS  PubMed  Google Scholar 

  54. Krishnaswami, S. R. et al. Using single nuclei for RNA-seq to capture the transcriptome of postmortem neurons. Nat. Protoc. 11, 499–524 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Wu, H., Kirita, Y., Donnelly, E. L. & Humphreys, B. D. Advantages of single-nucleus over single-cell RNA sequencing of adult kidney: rare cell types and novel cell states revealed in fibrosis. J. Am. Soc. Nephrol. 30, 23–32 (2019).

    Article  PubMed  Google Scholar 

  56. Bakken, T. E. et al. Single-nucleus and single-cell transcriptomes compared in matched cortical cell types. PLOS ONE 13, e0209648 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Luecken, M. D. & Theis, F. J. Current best practices in single-cell RNA-seq analysis: a tutorial. Mol. Syst. Biol. 15, e8746 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Baran-Gale, J., Chandra, T. & Kirschner, K. Experimental design for single-cell RNA sequencing. Brief. Funct. Genomics 17, 233–239 (2018).

    Article  CAS  PubMed  Google Scholar 

  59. Hahn, B. H. et al. American College of Rheumatology guidelines for screening, treatment, and management of lupus nephritis. Arthritis Care Res. 64, 797–808 (2012).

    Article  Google Scholar 

  60. Debey, S. et al. A highly standardized, robust, and cost-effective method for genome-wide transcriptome analysis of peripheral blood applicable to large-scale clinical trials. Genomics 87, 653–664 (2006).

    Article  CAS  PubMed  Google Scholar 

  61. Menon, R. et al. Gender-based blood transcriptomes and interactomes in multiple sclerosis: involvement of SP1 dependent gene transcription. J. Autoimmun. 38, J144–J155 (2012).

    Article  CAS  PubMed  Google Scholar 

  62. Schwartzman-Morris, J. & Putterman, C. Gender differences in the pathogenesis and outcome of lupus and of lupus nephritis. Clin. Dev. Immunol. 2012, 604892 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Lewis, M. J. & Jawad, A. S. The effect of ethnicity and genetic ancestry on the epidemiology, clinical features and outcome of systemic lupus erythematosus. Rheumatology 56, i67–i77 (2017).

    Article  CAS  PubMed  Google Scholar 

  64. Freedman, B. I. et al. End-stage renal disease in African Americans with lupus nephritis is associated with APOL1. Arthritis Rheumatol. 66, 390–396 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Der, E. et al. Tubular cell and keratinocyte single-cell transcriptomics applied to lupus nephritis reveal type I IFN and fibrosis relevant pathways. Nat. Immunol. 20, 915–927 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Chang, A. et al. In situ B cell-mediated immune responses and tubulointerstitial inflammation in human lupus nephritis. J. Immunol. 186, 1849–1860 (2011).

    Article  CAS  PubMed  Google Scholar 

  67. Winchester, R. et al. Immunologic characteristics of intrarenal T cells: trafficking of expanded CD8+ T cell beta-chain clonotypes in progressive lupus nephritis. Arthritis Rheum. 64, 1589–1600 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Angerer, P. et al. destiny: diffusion maps for large-scale single-cell data in R. Bioinformatics 32, 1241–1243 (2016).

    Article  CAS  PubMed  Google Scholar 

  69. Qiu, X. et al. Reversed graph embedding resolves complex single-cell trajectories. Nat. Methods 14, 979–982 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Meng, X. M., Mak, T. S. & Lan, H. Y. Macrophages in renal fibrosis. Adv. Exp. Med. Biol. 1165, 285–303 (2019).

    Article  PubMed  Google Scholar 

  71. Karnell, J. L. et al. Role of CD11c(+) T-bet(+) B cells in human health and disease. Cell Immunol. 321, 40–45 (2017).

    Article  CAS  PubMed  Google Scholar 

  72. Jenks, S. A. et al. Distinct effector B cells induced by unregulated toll-like receptor 7 contribute to pathogenic responses in systemic lupus erythematosus. Immunity 49, 725–739 e6 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Naradikian, M. S., Hao, Y. & Cancro, M. P. Age-associated B cells: key mediators of both protective and autoreactive humoral responses. Immunol. Rev. 269, 118–129 (2016).

    Article  CAS  PubMed  Google Scholar 

  74. Wang, S. et al. IL-21 drives expansion and plasma cell differentiation of autoreactive CD11c(hi)T-bet(+) B cells in SLE. Nat. Commun. 9, 1758 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Zotos, D. et al. IL-21 regulates germinal center B cell differentiation and proliferation through a B cell-intrinsic mechanism. J. Exp. Med. 207, 365–378 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Cole, C., Byrne, A., Beaudin, A. E., Forsberg, E. C. & Vollmers, C. Tn5Prime, a Tn5 based 5′ capture method for single cell RNA-seq. Nucleic Acids Res. 46, e62 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Villani, A. C. et al. Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors. Science 356, eaah4573 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Zhang, Z., Kyttaris, V. C. & Tsokos, G. C. The role of IL-23/IL-17 axis in lupus nephritis. J. Immunol. 183, 3160–3169 (2009).

    Article  CAS  PubMed  Google Scholar 

  79. Amarilyo, G., Lourenco, E. V., Shi, F. D. & La Cava, A. IL-17 promotes murine lupus. J. Immunol. 193, 540–543 (2014).

    Article  CAS  PubMed  Google Scholar 

  80. Jacob, N. et al. Accelerated pathological and clinical nephritis in systemic lupus erythematosus-prone New Zealand Mixed 2328 mice doubly deficient in TNF receptor 1 and TNF receptor 2 via a Th17-associated pathway. J. Immunol. 182, 2532–2541 (2009).

    Article  CAS  PubMed  Google Scholar 

  81. Lee, S. Y. et al. Inhibition of IL-17 ameliorates systemic lupus erythematosus in Roquin(san/san) mice through regulating the balance of TFH cells, GC B cells, Treg and Breg. Sci. Rep. 9, 5227 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Schmidt, T. et al. Function of the Th17/interleukin-17A immune response in murine lupus nephritis. Arthritis Rheumatol. 67, 475–487 (2015).

    Article  CAS  PubMed  Google Scholar 

  83. Tilstra, J. S. et al. Kidney-infiltrating T cells in murine lupus nephritis are metabolically and functionally exhausted. J. Clin. Invest. 128, 4884–4897 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  84. McKinney, E. F., Lee, J. C., Jayne, D. R., Lyons, P. A. & Smith, K. G. T-cell exhaustion, co-stimulation and clinical outcome in autoimmunity and infection. Nature 523, 612–616 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Kok, H. M. et al. Systemic and local granzyme B levels are associated with disease activity, kidney damage and interferon signature in systemic lupus erythematosus. Rheumatology 56, 2129–2134 (2017).

    Article  CAS  PubMed  Google Scholar 

  86. Bleul, C. C. et al. The lymphocyte chemoattractant SDF-1 is a ligand for LESTR/fusin and blocks HIV-1 entry. Nature 382, 829–833 (1996).

    Article  CAS  PubMed  Google Scholar 

  87. Oberlin, E. et al. The CXC chemokine SDF-1 is the ligand for LESTR/fusin and prevents infection by T-cell-line-adapted HIV-1. Nature 382, 833–835 (1996).

    Article  CAS  PubMed  Google Scholar 

  88. Togel, F., Isaac, J., Hu, Z., Weiss, K. & Westenfelder, C. Renal SDF-1 signals mobilization and homing of CXCR4-positive cells to the kidney after ischemic injury. Kidney Int. 67, 1772–1784 (2005).

    Article  PubMed  Google Scholar 

  89. Griffith, J. W., Sokol, C. L. & Luster, A. D. Chemokines and chemokine receptors: positioning cells for host defense and immunity. Annu. Rev. Immunol. 32, 659–702 (2014).

    Article  CAS  PubMed  Google Scholar 

  90. Devarapu, S. K. et al. Dual blockade of the pro-inflammatory chemokine CCL2 and the homeostatic chemokine CXCL12 is as effective as high dose cyclophosphamide in murine proliferative lupus nephritis. Clin. Immunol. 169, 139–147 (2016).

    Article  CAS  PubMed  Google Scholar 

  91. Imai, T. & Yasuda, N. Therapeutic intervention of inflammatory/immune diseases by inhibition of the fractalkine (CX3CL1)-CX3CR1 pathway. Inflamm. Regen. 36, 9 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Aljaberi, N., Bennett, M., Brunner, H. I. & Devarajan, P. Proteomic profiling of urine: implications for lupus nephritis. Expert. Rev. Proteom. 16, 303–313 (2019).

    Article  CAS  Google Scholar 

  93. Huang, N. & Perl, A. Metabolism as a target for modulation in autoimmune diseases. Trends Immunol. 39, 562–576 (2018).

    Article  CAS  PubMed  Google Scholar 

  94. Jafari Ghods, F., Topal Sarikaya, A., Arda, N. & Hamuryudan, V. MiRNA and mRNA profiling in systemic lupus reveals a novel set of cytokine - related miRNAs and their target genes in cases with and without renal involvement. Kidney Blood Press. Res. 42, 1322–1337 (2017).

    Article  CAS  PubMed  Google Scholar 

  95. Peterson, V. M. et al. Multiplexed quantification of proteins and transcripts in single cells. Nat. Biotechnol. 35, 936–939 (2017).

    Article  CAS  PubMed  Google Scholar 

  96. Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Gray, D. H., Chidgey, A. P. & Boyd, R. L. Analysis of thymic stromal cell populations using flow cytometry. J. Immunol. Methods 260, 15–28 (2002).

    Article  CAS  PubMed  Google Scholar 

  98. Moor, A. E. & Itzkovitz, S. Spatial transcriptomics: paving the way for tissue-level systems biology. Curr. Opin. Biotechnol. 46, 126–133 (2017).

    Article  CAS  PubMed  Google Scholar 

  99. Patel, S. S. & Rodig, S. J. Overview of tissue imaging methods. Methods Mol. Biol. 2055, 455–465 (2020).

    Article  CAS  PubMed  Google Scholar 

  100. Nowling, T. K. & Gilkeson, G. S. Mechanisms of tissue injury in lupus nephritis. Arthritis Res. Ther. 13, 250 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  101. Oleinika, K., Mauri, C. & Salama, A. D. Effector and regulatory B cells in immune-mediated kidney disease. Nat. Rev. Nephrol. 15, 11–26 (2019).

    Article  CAS  PubMed  Google Scholar 

  102. Lech, M. & Anders, H. J. The pathogenesis of lupus nephritis. J. Am. Soc. Nephrol. 24, 1357–1366 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Daniel, C. et al. Extracellular DNA traps in inflammation, injury and healing. Nat. Rev. Nephrol. 15, 559–575 (2019).

    Article  CAS  PubMed  Google Scholar 

  104. Adam, M., Potter, A. S. & Potter, S. S. Psychrophilic proteases dramatically reduce single-cell RNA-seq artifacts: a molecular atlas of kidney development. Development 144, 3625–3632 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Brunner, H. I. et al. Urine biomarkers of chronic kidney damage and renal functional decline in childhood-onset systemic lupus erythematosus. Pediatr. Nephrol. 34, 117–128 (2019).

    Article  PubMed  Google Scholar 

  106. Pejchinovski, M. et al. Urine peptidomic biomarkers for diagnosis of patients with systematic lupus erythematosus. Lupus 27, 6–16 (2018).

    Article  CAS  PubMed  Google Scholar 

  107. Rovin, B. H. et al. Urine chemokines as biomarkers of human systemic lupus erythematosus activity. J. Am. Soc. Nephrol. 16, 467–473 (2005).

    Article  CAS  PubMed  Google Scholar 

  108. Schwartz, N. et al. Urinary TWEAK as a biomarker of lupus nephritis: a multicenter cohort study. Arthritis Res. Ther. 11, R143 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Soliman, S. & Mohan, C. Lupus nephritis biomarkers. Clin. Immunol. 185, 10–20 (2017).

    Article  CAS  PubMed  Google Scholar 

  110. Enghard, P. et al. CXCR3+CD4+ T cells are enriched in inflamed kidneys and urine and provide a new biomarker for acute nephritis flares in systemic lupus erythematosus patients. Arthritis Rheum. 60, 199–206 (2009).

    Article  CAS  PubMed  Google Scholar 

  111. Kopetschke, K. et al. The cellular signature of urinary immune cells in lupus nephritis: new insights into potential biomarkers. Arthritis Res. Ther. 17, 94 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  112. Scott, E., Dooley, M. A., Vilen, B. J. & Clarke, S. H. Immune cells and type 1 IFN in urine of SLE patients correlate with immunopathology in the kidney. Clin. Immunol. 168, 16–24 (2016).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

All authors are supported by the Accelerated Medicines Partnership (AMP) NIH UH2 AR06768. D.A.R. is supported by the Lupus Research Alliance, Burroughs Wellcome Fund Career Award for Medical Sciences and NIH NIAMS K08 AR-072791-01.

Author information

Authors and Affiliations

Authors

Contributions

All authors researched data for the article, wrote the manuscript, made substantial contributions to discussions of the content and reviewed or edited the manuscript before submission.

Corresponding author

Correspondence to Betty Diamond.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information

Nature Reviews Nephrology thanks T. M. Chan, V. Kyttaris and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Glossary

Unsupervised clustering

Unbiased approach used to identify sets of samples that share similar gene expression signatures.

Laser capture microscopy

A method that uses microscopy to identify and selectively collect specific regions of a tissue section for downstream analyses such as RNA expression measurement.

Computational deconvolution

In RNA sequencing analysis, an effort to quantify or identify features of individual cell populations from bulk RNA sequencing data using cell type-specific reference datasets.

Cryopreservation

The process of freezing a sample (for example, tissue or cells) for long-term storage, often using a controlled freezing method in the presence of a cryoprotectant such as dimethyl sulfoxide to preserve cell viability during freeze and thaw.

Proliferative nephritis

A form of nephritis characterized histologically by endothelial and mesangial proliferation within glomeruli.

Membranous nephritis

A form of nephritis characterized by thickening of the glomerular capillary wall with subepithelial deposits but without cellular proliferative changes.

Saturation analysis

A procedure to estimate the extent to which a given sample represents a studied population. Typically, this analysis involves down-sampling the original data, computing a statistic of interest (for example, the number of discovered clusters) and estimating its rate of change across a sequence of sample sizes.

Plasmablasts

A differentiated B cell population specialized in the secretion of antibodies; often a precursor to plasma cells.

5′ scRNA-seq

Single-cell RNA sequencing (scRNA-seq) methods that sequence RNA transcripts from the 5′ end, allowing clearer analysis of the 5′ end of RNA transcripts, as opposed to the more common strategies of RNA-seq methods that analyse transcripts from the 3′ end.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rao, D.A., Arazi, A., Wofsy, D. et al. Design and application of single-cell RNA sequencing to study kidney immune cells in lupus nephritis. Nat Rev Nephrol 16, 238–250 (2020). https://doi.org/10.1038/s41581-019-0232-6

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41581-019-0232-6

  • Springer Nature Limited

This article is cited by

Navigation